class_docstr: Policy that derives its actions from a learned Q function.
  
  The action returned is the one that yields the highest Q value for
  a given state, as determined by the supplied Q function.
  
  Args:
      qf (object): Q network.
      env_spec (EnvSpec): Environment specification.
      name (str): Name of this policy.
code: def __init__(self, qf, env_spec, name="DiscreteQFArgmaxPolicy"):
      super().__init__(env_spec, name)
      self._qf = qf
  
file_path: garage/src/garage/torch/policies/discrete_qf_argmax_policy.py
imports: import numpy
  import torch
  from garage.torch import np_to_torch
  from garage.torch.policies.policy import Policy
label_desc: Use image node_id 1 to create a new DiscreteQFArgmaxPolicy object from inherited base classes: Policy with example: obj = DiscreteQFArgmaxPolicy(qf, env_spec, name)